Final Report: Carolina Center for Computational Toxicology

EPA Grant Number: R833825
Center: Carolina Center for Computational Toxicology
Center Director: Rusyn, Ivan
Title: Carolina Center for Computational Toxicology
Investigators: Rusyn, Ivan , Wright, Fred A. , Tropsha, Alex , Nobel, Andrew , Gomez, Shawn , Elston, Timothy , Sun, Wei
Institution: University of North Carolina at Chapel Hill , North Carolina State University
EPA Project Officer: Hahn, Intaek
Project Period: April 1, 2008 through March 31, 2012
Project Amount: $3,400,000
RFA: Computational Toxicology Centers: Development Of Predictive Environmental And Biomedical Computer-Based Simulations And Models (2007) RFA Text |  Recipients Lists
Research Category: Computational Toxicology , Human Health , Safer Chemicals

Objective:

Project 1

Predictive Modeling of Chemical-Perturbed Regulatory Networks in Systems Toxicology

This project was focused on the creation and development of computational methods and models, with a particular focus on better understanding molecular networks and how these networks respond to chemical perturbations. The results of this work will be used to both define molecular network architectures relevant to toxicology as well as model their dynamical behavior.

Project 2

Toxico-genetic Modeling: Population-wide Predictions from Toxicity Profiling

This project was focused exclusively on the development of tools for the analysis of the toxicological data in genetically diverse subjects by taking into account the information on high-density (up to millions of SNPs) genetic maps of the test populations. The results will be used to provide toxico-genetic context to the development of mechanistic, predictive, and statistical models for computational toxicology (Rusyn and Daston, 2010).

Project 3

Development of Validated and Predictive Quantitative Structure-Toxicity Relationship Models that Employ both Chemical and Biological Descriptors of Molecular Structures and Take into Account Genetic Diversity Between Individuals

This project was testing a hypothesis that the accuracy and interpretability of a computational model to predict chemical toxicity will be improved by including the results of short term biological assays as additional, “biological” descriptors of chemicals in combination with the traditional chemical descriptors (Zhu, et al., 2008). Based on our expertise in cheminformatics, we have expanded the concept of in vitro-in vivo extrapolation to include the explicit knowledge of chemical structure formulating the challenge of chemical toxicology as “structure– in vitro-in vivo extrapolation.” During the course of this project, we have implemented several types of computational workflows that express this concept in the form of computational algorithms (Rusyn, et al., 2012) as well as conducted and published multiple studies in support of this hypothesis.

Summary/Accomplishments (Outputs/Outcomes):

Project 1

Specific Objective 1: Development and application of data-driven methods for the inference and high-level modeling of regulatory network response to chemical perturbation.

Understanding how chemical perturbations may lead to phenotypic changes requires a picture of how the underlying molecular networks are “wired” as well as a means of quantifying the differences in network architectures after this perturbation. Relevant to chemically induced cancers and developmental disorders, this work has fostered a range of approaches and we have established a variety of methods for the prediction of molecular interaction networks (Choi and Gomez, 2009; Doolittle and Gomez, 2010, 2011). As predicting protein interactions is a potentially slightly easier task than inference of chemical-protein interactions, we initially focused on data mining and structural-based approaches for predicting protein network architecture. Analogous to chemical systems, we tested these methods on host-pathogen systems where an “external” molecule exerted its effect on the host (human) molecular network. We have since mapped these approaches to chemical-protein interactions, providing a novel approach for mapping relevant network architectures.

Expanding on a related aspect of this work, the development of novel methods (xCEED) for the comparison of graph/tree structures (Choi and Gomez, 2009) provides a unique way to quantify similarities and/or differences between molecular signaling networks. This family of methods has a wide range of applications, from helping to infer protein interactions to the comparison of pathway/network structures. These comparisons are an important component of future toxicity studies where understanding and comparing the relevance of experimental results across species (e.g.,. comparison of rat/mouse studies to human data) is essential.

As chemicals can be described structurally in terms of a network, maximum common subgraph algorithms are a mainstay of chemical informatics applications. While the previous xCEED approaches provide some capability in this area, we since have developed a novel graph algorithm approach (in preparation) that provides a powerful new capability to describe and compare network structures, both small-scale chemicals as well as very large-scale molecular networks, and quantify their differences and similarities. These approaches are fundamental in the prediction of where such chemical structures may best “plug in” to the human molecular network within the cell.

The complexity of data sets such as found in ToxCast, require the development of novel approaches for data integration, analysis and interpretation. We have developed dimension reduction and data mining approaches that allow the integration of different data types (assays, chemicals and endpoints) and their downstream interpretation as “functional modules” that define chemical activity networks and provide a novel means of analysis (Choi, et al, 2009; Staab, et al, 2009; Gomez, 2011). In addition, we have worked with other center members on approaches that utilize known biological pathway information to enhance QSAR predictions (Zhu, et al., 2011).

We are working with a number of groups on the collection of data describing metabolic changes in the face of chemical perturbation. Supporting this, we developed a novel method for the alignment of NMR spectra that are typical outputs of systems-level metabolomic analyses (O’Connell, et al., 2009; Staab, et al., 2010). This approach (termed PCANS –Progressive Consensus Alignment of NMR Spectra) improves the analysis of metabolomic data sets that are relevant in the analysis of toxicity-relevant data sets.

Application of these and related tools to cancer systems having well-defined experimental systems, known chemical interventions, and a wealth of background information is leading to significant insight into how perturbations to cellular molecular networks lead to the presentation of disease as well as potential methods for ameliorating these perturbations through alterations in network wiring (Abell, et al., 2011; Duncan, et al., 2012).

Specific Objective 2: Mechanistic modeling of nuclear receptors.

The original goal of this aim was to develop mechanistic models describing nuclear receptor function. As originally proposed, the data for this model would be derived from literature data and from associated center members. However, in the course of initial model investigation, we found that the data available was generally inadequate for the level of mechanistic model development we desired. In addition, provided ToxCast generated data could not generally be used to inform such models.

Parallel efforts, however, led to the development of powerful large-scale physiological model of metabolism (Xu, et al., 2011b). This model describes, at a mechanistic level, metabolism between liver, adipose, muscle and blood compartments. Primary detail is provided at the level of metabolic pathways within the liver. This model has been validated through comparisons to experimental measurements of metabolism in the mouse and rat model animal systems at the cellular and whole-liver levels. Complimentary to PK/PD models, this model provides insight into the dynamics of the metabolic networks at the molecular level; where the action of chemicals of interest exert their effect. In a related capacity, this model helps in the interpretation of fasting effects on gene expression, which is a potentially important confounding variable in toxicity studies. In collaboration with Ingenuity Systems Inc., we established a 10-gene signature that can serve as a diagnostic flag signaling the possible presence of confounding due to fasting effects (Xu, et al., 2011a).

Extension of this work has led to the establishment of a model for glutathione metabolism (Jeffries, et al., 2012). Glutathione is a key metabolic player in toxicity metabolism, acting as an antioxidant as well as within the detoxification processes. The model we have developed has been supported by experimental data derived from a NMR bioreactor housing human hepatocytes, with metabolite data samples capable of being collected once every minute. Exposing these cells to bromobimane, the model has now undergone significant parameter fitting and validation and provides a power capability for the modeling of a key detoxification pathway that is described within the context of whole human body metabolism. Models of airway epithelia, DNA damage and/or involving complex boundaries have similarly been developed (Herschlag, et al., 2013; Strychalski, et al., 2010; Garcia, et al., 2011; Kesseler, et al., 2013).

Together, these models form the foundation for the modeling of key detoxification pathways and their effect on metabolism and physiology. They are now being applied in a recent pilot study in Diversity Outbred (DO) mice where we have collected 50 liver and serum samples from this genetically heterogeneous population and identified metabolites using the Metabolon metabolomics platform. Over 300 metabolites have been identified in both tissue types and these measurements are now being used in model development as well as a proof-of-concept for understanding metabolic network perturbation in the context of genetic variation.

Specific Objective 3: Integration and deployment of high- (Specific Objective 1) and low- (Specific Objective 2) modeling tools.

As described in the original proposal, a major challenge is in making modeling tools practical for investigators with a range of backgrounds and areas of expertise. To enhance the broad use and effectiveness of the tools developed in this work, we have provided them in an open-source format and provided extensive support for their further use. Longer-term, we are working on translating some of these tools into web applications, which the goal of further enhancing their broader usability.

Our whole-body physiological model of liver/hepatocyte-focused metabolism is being provided as MATLAB code that is distributed, along with the publication, on the PLoS Computational Biology website. In addition, the simulation code is available as an SBML (Systems Biology Markup Language) representation to facility use by a wider range of investigators. We have consulted with several groups on its use and implementation. As an example, we have provided this code to Gary Churchill of the Jackson Laboratory and have consulted with him and his group on its use as both a research tool as well as its use in a potential educational application. This is a continuing collaboration and is being further developed in the context of studying genetic variation (utilizing mouse models) and its role in metabolism and linkage to chemical perturbation.

The xCEED tools for network/graph comparison have been distributed both on our website as well as a bitbucket repository (https://bitbucket.org/gomezlab/xceed). In collaboration with downstream users, we have improved aspects of usability and presentation of results since its initial publication and these changes are part of the bitbucket distribution.

The PCANS software for improved analysis of NMR spectra commonly acquired from toxicity studies, is provided on our website. This code and a prototype web application led to feedback from users that led to a new version with an improved ability to pick peaks from spectra. In addition, due to potentially high computational demands, the ability to run on Amazon Web Services has been developed. This new version will be distributed through a public data repository such as bitbucket or github.

Additional Outcomes.

While not an original objective of this project, we found that a significant limitation in the development of relevant mechanistic toxicity models was the lack of dynamic measurements — i.e., those that explicitly quantify at how these systems change as a function of time (in parallel with factors such as dose). We have developed a set of image analysis tools that that allow the quantification of cell/molecular behaviors captured during live cell microscopy studies (Berginski, et al., 2011; Chen, et al., 2013; Chaki, et al., 2013). Longer-term, these technology platforms provide the ability to perform high-throughput quantification of the effects of chemical exposure on the dynamic behaviors of molecular networks within individual cells and cell populations.

Project 2

Specific Objective 1: Develop toxico-genetic expression quantitative trait loci (eQTL) mapping tools, perform transcription factor network inference and integrative pathway assessment.

1.1. Fast SNP-phenotype correlation methods

Expression quantitative trait locus (eQTL) analysis has moved from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. This project was developing convenient computational tools that can be used for discovery of eQTL, or to interpret genome-wide association study results (Wright, et al., 2012).

The fast-SNP correlation method, called FastMap (Gatti, et al., 2009b), works for 2-genotype populations (i.e., crosses between inbred rodent strains). The approach increases the speed of eQTL mapping compared to existing methods by several orders of magnitude, enabling the use of permutation-based inference, which can provide superior error control. We also have created a human version of FastMap, i.e., a version that can analyze three-SNP genotypes (available from http://comptox.unc.edu/fastmap.php). We also have been working on multiple testing correction approaches, which are among the most important statistical problems in eQTL analysis. We have proposed a geometric interpretation of permutation p-values, and have developed an efficient permutation p-value estimation method based on this geometric interpretation (Sun and Wright, 2010).

In genome-wide association studies, population stratification is recognized as producing inflated type I error due to the inflation of test statistics. We showed that principal component-based methods applied to genotypes provide information about population structure, and can be used to control for stratification (Zou, et al., 2010). To address this challenge with a computational tool, we developed EigenCorr, which selects principal components based on both their eigenvalues and their correlation with the (disease) phenotype. Our approach tends to select fewer principal components for stratification control than does testing of eigenvalues alone, providing substantial computational savings and improvements in power. Analyses of simulated and real data demonstrate the usefulness of the proposed strategy (Lee, et al., 2011).

In order to incorporate sequence-based expression (RNA-Seq) data into our analysis pipelines we have been developing more powerful approaches to handle transcript count data. A hallmark of such datasets is that they show strong evidence of overdispersion, and must be modeled using beta-binomial or similar distributions. We have developed a new package, BBSeq (Zhou, et al., 2011), which performs this modeling, and also attempts to maximize power for small samples by modeling the relationship between the mean and the overdispersion. We also described a framework for handling RNA-Seq data in eQTL analysis (Sun, 2012; Sun and Hu, 2013; Sun, et al., 2009b).

eQTL heritability provides directly useful information for prioritizing transcripts for potential genetic variation in susceptibility. In a partnership with multiple investigators, we also have developed the seeQTL eQTL browser (Xia, et al., 2012) that has more extensive search and display capabilities than competing browsers.

1.2. Integration of eQTL and transcriptional network regulation

A major emphasis of our work has been on the integrated study of eQTL and transcription regulation. In addition to genetic variation, gene expression is regulated by many other factors, such as nucleosome occupation and transcription factor binding. Incorporation of these factors would lead to better understanding of the genetic basis of gene expression, which is an important issue in toxicogenomics. However, efficient and accurate quantification of nucleosome occupation and transcription factor binding are not trivial. We have developed two methods for these purposes (Sun, et al., 2009a; Sun, et al., 2009c).

Another source of computational difficulty arises in performing genetic pathway analysis. We and others have repeatedly shown that methods in which individual expression profiles are permuted relative to clinical phenotypes, as is performed by our SAFE tool (Gatti, et al., 2010a; Gatti, et al., 2009c), are superior to competing methods. However, the approaches can be time-consuming and memory intensive. We have developed the safeExpress procedure (Zhou, et al., 2013) to overcome this obstacle, using mathematical approximations to accurately mimic permutation testing without requiring actual permutation.

Specific Objective 2: Perform toxico-genetic modeling of liver toxicity in cultured mouse hepatocytes.

In the past 5 years, genetically defined mouse models have been combined with the limited data from human studies to not only discover the genetic determinants of susceptibility, but also to understand the molecular underpinnings of toxicity (Rusyn, et al., 2010). While our in vivo studies have been exploring the role that genetic variability may play in the mechanisms of chemical toxicity (Bradford, et al., 2011; Gatti, et al., 2009a; Gatti, et al., 2011; Gatti, et al., 2010b; Harrill, et al., 2009), the throughput of the studies in experimental animals is low.

Over the years, various liver-derived in vitro model systems have been developed to enable investigation of the potential adverse effects of chemicals and drugs (Soldatow, et al., 2013). We have been working to establish a toxico-genetic model of liver toxicity in cultured mouse hepatocytes (Martinez, et al., 2009). We focused our efforts on standardization of cell isolation and culture conditions, and determination of whether the near-physiological maintenance of the cells isolated from different mouse inbred strains can be achieved and assess whether the reproducibility of functionality can be attained within a given strain over subsequent isolations. In a study of primary mouse hepatocytes from 15 strains we observed that cell function of hepatocytes isolated from different strains and cultured under standardized conditions is comparable and cells remain viable and metabolically active (Martinez, et al., 2010). These experiments open new opportunities for high-throughput and low-cost in vitro assays that may be used for studies of toxicity in a genetically diverse population.

Specific Objective 3: Discover chemical-induced regulatory networks using population-based toxicity phenotyping in human cells.

We have been working to establish a human in vitro population-wide toxicity testing system by using lymphoblast cell lines from the 1000 Genomes and HapMap projects. In our first study, we collected in vitro population-based toxicity phenotyping data (cell viability and apoptosis) on 14 EPA-relevant chemicals (pesticide actives, plasticizers, polychlorinated biphenyls, etc.) in a panel of densely genotyped 87 HapMap CEU human lymphoblastoid cell lines (Hatcher, et al., 2009; O’Shea, et al., 2011a; O’Shea, et al., 2010; O’Shea, et al., 2011b). This work has established the degree of inter-individual variability in responses to these agents, examined whether such variability is heritable, and conducted genome-wide association analysis of the individual chemical-assay phenotypes. This work served as an important proof of principle in establishing the utility of in vitro toxicity screening in a genetically-defined population model, as well serving as a testing ground for the combination of toxicity phenotypes with baseline expression and genotype data.

Our initial success with this novel experimental approach has led to a much more ambitious collaboration with the Tox21 partner organizations, the National Toxicology Program and the NIH Chemical Genomics Center. In our second study, the population-based qHTS screening paradigm was explored in which hundreds of compounds may be profiled rapidly in dozens of cell lines and multiple biological targets. Our goal was to obtain data for predictive in vitro models of chemical-induced toxicity anchored on inter-individual genetic variability. Eighty one human lymphoblast cell lines from 27 CEPH trios were exposed to 240 chemicals at 12 concentrations and cell viability and apoptosis were evaluated (Lock, et al., 2012).

In our third study, also in collaboration with NCGC and NTP, we successfully screened 1086 human lymphoblast cell lines, representing 9 populations from 5 continents, in a cell viability assay with 179 diverse environmental chemicals at 8 concentrations (Abdo, et al., 2012; Abdo et al. 2013). The extent of inter-individual variability was less than 10 fold for about 2/3 of the 179 compounds; however, some compounds showed more than a 100-fold range. Across all 179 compounds, clustering of EC10 profiles revealed similarity of responses in cell lines according to the genetic ancestry. For individual chemicals, population differences were evident for about 40% of the compounds. Heritability analysis, using populations with parent-child trios, showed that cytotoxicity responses of 22 chemicals were significantly heritable (~20-40%). Because cell lines used in this study have been densely genotyped or sequenced, analyses of genome-wide association, and association pathway analyses, were performed to identify significant associations between variants/genes/pathways and cytotoxicity. Across the 179 compounds, 142 polymorphisms were identified as suggestive of the genetic association. Of the 1086 screened cell lines, basal gene expression (RNA sequencing-based) data is publicly available for 344 cell lines and we performed correlation analysis of this data with cytotoxicity across the 179 chemicals to identify additional genes and pathways that also may be associated with inter-individual differences in toxicity.

Overall, we conclude that genomic-anchored in vitro screening model using 1000 Genomes lymphoblast cell lines offers unique advantages for identifying variations in toxicity responses at the DNA sequence level and for defining experimentally-based confidence intervals for population variability.

Project 3 

Specific Objective 1: Develop rigorous end point toxicity predictors based on the QSAR modeling workflow and conventional chemical descriptors.

1.1. Models of mutagenicity

We have complied and curated the largest in the public domain database of more than 7,000 molecules tested in the Ames genotoxicity assay. We have formed international virtual collaboratory incorporating 14 QSAR modeling groups from eight countries which established that consensus QSAR model integrating individual predictions from individual laboratories achieved the highest statistical significance and external predictive power with the prediction accuracy exceeding 80% (Sushko, et al., 2010). In a recent follow-up of this study, our group has developed QSAR models of mutation-site specific genotoxicity. Large data sets for each mutation target DNA sequence were compiled and curated by including data from additional, less common test strains. The resulting validated sequence-specific models have higher accuracy (65-79%) than previous models for individual bacterial assays (51-81%) (Low, et al., 2013). In addition, we used carcinogenicity potency database to model carcinogen hazard (Wang, et al., 2009; Zhu, et al., 2008).

1.2. Models of environmental toxicity

We have modeled toxicity of 95 nitroaromatic compounds against the ciliate T. pyriformis. Multiple models have been generated with leave-one-out cross-validated R2=0.82–0.95 for the training sets and R2=0.57-0.86 for the test sets. The resulting models were employed for predicting toxicity of an external set of 63 nitroaromatics and R2ext=0.65 was obtained. Validated models were explored to (i) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (ii) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; (iii) analyze the role of various physical-chemical factors (e.g., electrostatics, hydrophobicity, hydrogen bonding, atomic identity, etc.) in compounds’ toxicity. Resulting models (Artemenko, et al., 2011) can be used in assessment of environmental hazard of nitroaromatic chemicals.

1.3. Models of skin sensitization

We recently have developed QSAR models of skin sensitization (tested in local lymph node assay) and compared their external predictivity with that of the OECD QSAR toolbox models. We applied kNN and RF algorithms to a dataset of 471 compounds, which was obtained from the 2009 annual report of Interagency Coordinating Committee on the Validation of Alternative Methods at NIEHS. In the course of external validation, our models demonstrated good sensitivity, specificity, and overall balanced accuracy (81-89%, 69-73%, and 77-79% respectively). Likewise evaluation by OECD QSAR toolbox showed prediction accuracy of only around 50%. We posit that our models will be useful in assessing skin sensitization potential of chemicals (Alves, et al., 2013; Pu, et al., 2011). We have made these models freely available for public use on our Chembench portal (chembench.mml.unc.edu).

1.4. Models of acute toxicity

In a collaborative study with EPA (Easterling, et al., 2009; Zhu, et al., 2009a; Zhu, et al., 2009b; Zhu, et al., 2009c; Zhu, et al., 2009d; Zhu and Tropsha, 2009) we have modeled the largest publicly available dataset of rat acute LD50. The dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled by EPA. The prediction accuracy for the external validation set ranged from 0.24 to 0.70 (linear regression coefficient R2) depending on the use of applicability domain thresholds. The best model is publicly available on the Chembench portal (chembench.mml.unc.edu).

Specific Objective 2: Develop novel computational toxicogenomic models based on combined chemical and biological descriptors through QSAR modeling workflow.

2.1. Development of models that combine qHTS screening and chemical descriptors

We have explored in vitro IC50 cytotoxicity values and in vivo rodent LD50 values for over 300 chemicals from the ZEBET database (Zhu, et al., 2009e). Conventional Quantitative Structure-Activity Relationship (QSAR) modeling of mouse or rat acute LD50 for ZEBET compounds yielded no statistically significant models. A novel two-step modeling approach was developed as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values. Second, conventional binary classification QSAR models are built to predict the group affiliation based on chemical descriptors only. Third, k-Nearest Neighbor (kNN) continuous QSAR models are developed for each sub-class to predict LD50 from chemical descriptors. Importantly, models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

To extend our previous successful QSAR modeling efforts using in vitro qHTS data, we aimed at incorporating dose-response data into the modeling workflow (Sedykh, et al., 2010, 2011). Cell viability qHTS data for 1,408 compounds in 13 cell lines have been deposited in PubChem providing the opportunity to study the relationship between in vitro and in vivo effects. Hybrid QSAR models developed with chemical and noise-filtered qHTS descriptors outperformed conventional QSAR models. This study provides another confirmation that adding biological data, such as dose-response qHTS profiles, to conventional chemical descriptors can improve predictive power of toxicity prediction models.

2.2. ToxCast data analysis and modeling

We have been actively engaged in the analysis of the ToxCast™ Phase I data. Utilizing chemical descriptors did not yield statistically significant and externally predictive QSAR models resulted from these efforts. We then applied hierarchical and hybrid QSAR approaches (Rusyn, et al., 2012) that combine the results of selected ToxCast in vitro bioassays with a subset of chemical descriptors. This, however, gave only limited improvement, indicating that Phase I data is lacking both chemical (structurally too diverse) and biological (reproducibility/noise issues) dimensions. Nevertheless, we showed (Tang, et al., 2010; Zhang, et al., 2009a; Zhang, et al., 2009b) that ToxCast in vitro assays can be used as biological profiles for establishing biological similarity between compounds, as well as for systematic mining of those in vitro assays with high correlation to ToxRefDB in vivo endpoints.

In (Golbraikh, et al., 2013a; Golbraikh, et al., 2013b; Golbraikh, et al., 2012a; Golbraikh, et al., 2012b), we have modeled ToxCast data, one example being C. elegans toxicity for 212 compounds from ToxCastTM Phase I. We have built a variety of models using chemical and biological (ToxCast assays) descriptors: individually and in combination (as hybrid descriptors). The best performing was a hybrid kNN model that had externally validated accuracy of 73% (at 100% coverage). Using stricter thresholds for prediction confidence improves performance further (but for a subset of chemicals): to 78% (at 55% coverage), and to 83% (at 35% coverage).

2.3. Using toxicogenomics data as biological predictors.

QSAR modeling and toxicogenomics are used independently as predictive tools in toxicology. In a study by (Low, et al., 2011a; Low, et al., 2011b), we compared several models predicting drug hepatotoxicity in rats based on different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The target property was hepatotoxicity level following 28 days of treatment, established by histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using chemical descriptors and several classification methods. Next, we built models with the same classification methods using only toxicogenomic data treated as biological descriptors. Finally, hybrid models combining both chemical descriptors and transcripts were developed. Although the accuracy of hybrid models did not exceed that of models based on toxicogenomic data, the use of both types of descriptors enriched the interpretation of the models. We concluded that concurrent exploration of chemical features and acute treatment-induced changes in gene transcript levels will (i) enrich the mechanistic understanding of subchronic liver injury and (ii) afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Specific Objective 3: Develop novel computational toxicogenetic models based on combined genetic, chemical and toxicity descriptors through QSAR-like modeling workflow.

Population-based toxicity screening has been conducted against 81 cell lines to test a dataset comprised of 240 compounds (in the previous studies, only 13 cell lines were explored). These compounds were selected from the 1,408 substances of the NTP screening library. Each compound was tested in each cell line using cell viability and caspase assays. Using this data, we have applied our standard modeling workflow to develop models of rat acute toxicity, Ames mutagenicity, and rodent carcinogenicity, using both conventional chemical descriptors as well as noise-treated qHTS data employed as biological descriptors (Sedykh, et al., 2012). Interestingly, biological descriptors alone afforded acceptable QSAR models (accuracy of 55-63%), although hybrid models were still the best performing (61-72%). Importantly, the benefit from using in vitro data was varying and corresponded to the complexity of in vivo endpoint (most benefit - for acute toxicity, carcinogenicity; least - for mutagenicity). Our population-based hybrid model of acute toxicity shows higher accuracy (72±2%) than the previously published models (68±2% on the same external set).

Administrative Core

The Administrative Core Unit was responsible for the overall planning and coordination of the Center’s research activities, fostering interdisciplinary interaction, providing leadership on public outreach/translation, managing the Quality Assurance, scheduling meetings of the Executive Committee and Internal and External Advisory Committees, as well as fiscal and resource management and planning. The Core worked closely with the Research Projects to ensure effective communication with the EPA, government agencies, the scientific community, and potential users of the technology developed by our research team.

With regards to administrative and public outreach functions, the core has been instrumental in maintaining very high level of interactions between the Research Projects by coordinating regular Center meetings. Seven to eight Center-wide meetings were held yearly. In addition, every year the Center held annual meetings with the External Advisory Board.

With regards to the communication and public outreach functions, the Center has established a web-site (http://comptox.unc.edu) which contains information about all components and an up-to-date listing of news items, PDFs and lists of all publications and abstracts, as well as the links to software tools developed. Furthermore, regular meetings with EPA NCCT personnel were held (every 3-6 months) by each of the Research Projects and the work of the Center was disseminated through scientific publications, as well as oral and poster presentations at the national and international meetings, including those organized by the EPA.


Journal Articles: 63 Displayed | Download in RIS Format

Other center views: All 98 publications 65 publications in selected types All 63 journal articles
Type Citation Sub Project Document Sources
Journal Article Abell AN, Jordan NV, Huang W, Prat A, Midland AA, Johnson NL, Granger DA, Mieczkowski PA, Perou CM, Gomez SM, Li L, Johnson GL. MAP3K4/CBP-regulated H2B acetylation controls epithelial-mesenchymal transition in trophoblast stem cells. Cell Stem Cell 2011;8(5):525-537.
abstract available   full text available
R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Cell Stem Cell-Full Text PDF
    Exit
  • Abstract: Cell Stem Cell-Abstract
    Exit
  • Other: ResearchGate-Abstract & Full Text
    Exit
  • Journal Article Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology 2015;284(2):262-272.
    abstract available   full text available
    R833825 (Final)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: Science Direct-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology 2015; 284(2):273-280.
    abstract available   full text available
    R833825 (Final)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: Science Direct-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Artemenko AG, Muratov EN, Kuz'min VE, Muratov NN, Varlamova EV, Kuz'mina AV, Gorb LG, Golius A, Hill FC, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR and QSAR in Environmental Research 2011;22(5-6):575-601.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Taylor & Francis-Abstract
    Exit
  • Journal Article Berginski ME, Vitriol EA, Hahn KM, Gomez SM. High-resolution quantification of focal adhesion spatiotemporal dynamics in living cells. PLoS ONE 2011;6(7):e22025 (13 pp.).
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS ONE-Full Text HTML
    Exit
  • Abstract: PLoS ONE-Abstract
    Exit
  • Other: PLoS ONE-Full Text PDF
    Exit
  • Journal Article Borysov P, Hannig J, Marron JS, Muratov E, Fourches D, Tropsha A. Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors. Journal of Chemometrics 2016;30(3):99-108.
    abstract available  
    R833825 (Final)
    R832720 (2009)
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Bradford BU, Lock EF, Kosyk O, Kim S, Uehara T, Harbourt D, DeSimone M, Threadgill DW, Tryndyak V, Pogribny IP, Bleyle L, Koop DR, Rusyn I. Interstrain differences in the liver effects of trichloroethylene in a multistrain panel of inbred mice. Toxicological Sciences 2011;120(1):206-217.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Chaki SP, Barhoumi R, Berginksi ME, Sreenivasappa H, Trache A, Gomez SM, Rivera GM. Nck enables directional cell migration through the coordination of polarized membrane protrusion with adhesion dynamics. Journal of Cell Science 2013;126(Pt 7):1637-1649.
    abstract available   full text available
    R833825 (Final)
  • Abstract from PubMed
  • Full-text: Journal of Cell Science-Full Text HTML
    Exit
  • Abstract: Journal of Cell Science-Abstract
    Exit
  • Other: Journal of Cell Science-Full Text PDF
    Exit
  • Journal Article Chen Z, Lessey E, Berginski ME, Cao L, Li J, Trepat X, Itano M, Gomez SM, Kapustina M, Huang C, Burridge K, Truskey G, Jacobson K. Gleevec, an Abl family inhibitor, produces a profound change in cell shape and migration. PLoS ONE 2013;8(1):e52233 (14 pp.).
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS ONE-Full Text HTML
    Exit
  • Abstract: PLoS ONE-Abstract
    Exit
  • Other: PLoS ONE-Full Text PDF
    Exit
  • Journal Article Choi K, Gomez SM. Comparison of phylogenetic trees through alignment of embedded evolutionary distances. BMC Bioinformatics 2009;10:423.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-Full Text PDF
    Exit
  • Journal Article Doolittle JM, Gomez SM. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virology Journal 2010;7:82 (15 pp.).
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-Full Text PDF
    Exit
  • Journal Article Doolittle JM, Gomez SM. Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Neglected Tropical Diseases 2011;5(2):e954 (15 pp.).
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS ONE-Full Text HTML
    Exit
  • Abstract: PLoS ONE-Abstract
    Exit
  • Other: PLoS ONE-Full Text PDF
    Exit
  • Journal Article Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, Johnson NL, Granger DA, Jordan NV, Darr DB, Usary J, Kuan P-F, Smalley DM, Major B, He X, Hoadley KA, Zhou B, Sharpless NE, Perou CM, Kim WY, Gomez SM, Chen X, Jin J, Frye SV, Earp HS, Graves LM, Johnson GL. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple negative breast cancer. Cell 2012;149(2):307-321.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Cell-Full Text HTML
    Exit
  • Abstract: Cell-Abstract
    Exit
  • Other: Cell-Full Text PDF
    Exit
  • Journal Article Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chemical Research in Toxicology 2010;23(1):171-183.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ACS-Full Text HTML
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling 2010;50(7):1189-1204.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ACS-Full Text HTML
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. Journal of Chemical Information and Modeling 2013;53(8):1915-1922.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ACS-Full Text HTML
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Gatti DM, Shabalin AA, Lam T-C, Wright FA, Rusyn I, Nobel AB. FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 2009;25(4):482-489.
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
    R832720 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Gatti DM, Sypa M, Rusyn I, Wright FA, Barry WT. SAFEGUI: resampling-based tests of categorical significance in gene expression data made easy. Bioinformatics 2009;25(4):541-542.
    abstract available   full text available
    R833825 (Final)
    R832720 (2008)
  • Abstract from PubMed
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Gatti DM, Harrill AH, Wright FA, Threadgill DW, Rusyn I. Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mammalian Genome 2009;20(7):437-446.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Springer - Abstract
    Exit
  • Journal Article Gatti DM, Zhao N, Chesler EJ, Bradford BU, Shabalin AA, Yordanova R, Lu L, Rusyn I. Sex-specific gene expression in the BXD mouse liver. Physiological Genomics 2010;42(3):456-468.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Full-text: APS-Full Text HTML
    Exit
  • Abstract: APS-Abstract
    Exit
  • Other: APS-Full Text PDF
    Exit
  • Journal Article Gatti DM, Barry WT, Nobel AB, Rusyn I, Wright FA. Heading down the wrong pathway: on the influence of correlation within gene sets. BMC Genomics 2010;11:574.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-Full Text PDF
    Exit
  • Journal Article Gatti DM, Lu L, Williams RW, Sun W, Wright FA, Threadgill DW, Rusyn I. MicroRNA expression in the livers of inbred mice. Mutation Research 2011;714(1-2):126-133.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDIrect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Golbraikh A, Muratov E, Fourches D, Tropsha A. Data set modelability by QSAR. Journal of Chemical Information and Modeling 2014;54(1):1-4.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ACS-Full Text HTML
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Ha MJ, Sun W. Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation. Biometrics 2014;70(3):765-773.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Harrill AH, Ross PK, Gatti DM, Threadgill DW, Rusyn I. Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicological Sciences 2009;110(1):235-243.
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Toxicological Sciences-Full Text HTML
    Exit
  • Abstract: Toxicological Sciences-Abstract
    Exit
  • Other: Toxicological Sciences-Full Text PDF
    Exit
  • Journal Article Herschlag G, Garcia GJ, Button B, Tarran R, Lindley B, Reinhardt B, Elston TC, Forest MG. A mechanochemical model for auto-regulation of lung airway surface layer volume. Journal of Theoretical Biology 2013;325:42-51.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDIrect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Kesseler KJ, Blinov ML, Elston TC, Kaufmann WK, Simpson DA. A predictive mathematical model of the DNA damage G2 checkpoint. Journal of Theoretical Biology 2013;320:159-169.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Lee S, Wright FA, Zou F. Control of population stratification by correlation-selected principal components. Biometrics 2011;67(3):967-974.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract
    Exit
  • Abstract: Wiley Online-Abstract
    Exit
  • Journal Article Lock EF, Abdo N, Huang R, Xia M, Kosyk O, O'Shea SH, Zhou YH, Sedykh A, Tropsha A, Austin CP, Tice RR, Wright FA, Rusyn I. Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Toxicological Sciences 2012;126(2):578-588.
    abstract available   full text available
    R833825 (Final)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry 2014;14(11):1356-1364.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
    R835166 (2014)
    R835166 (2016)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Bentham Science-Abstract
    Exit
  • Journal Article Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz’min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology 2011;24(8):1251-1262.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: ACS Publications-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Martinez SM, Bradford BU, Soldatow VY, Kosyk O, Sandot A, Witek R, Kaiser R, Stewart T, Amaral K, Freeman K, Black C, LeCluyse EL, Ferguson SS, Rusyn I. Evaluation of an in vitro toxicogenetic mouse model for hepatotoxicity. Toxicology and Applied Pharmacology 2010;249(3):208-216.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Khristova T, Kuz'min VE, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. QSAR analysis of [(biphenyloxy)propyl] isoxazoles: agents against coxsackievirus B3. Future Medicinal Chemistry 2011;3(1):15-27.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Abstract from PubMed
  • Full-text: ResearchGate-Full Text HTML
    Exit
  • Abstract: FutureScience-Abstract
    Exit
  • Other: ResearchGate-Full Text PDF
    Exit
  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and developing approaches for QSAR analysis of mixtures. Molecular Informatics 2012;31(3-4):202-221.
    abstract available  
    R833825 (Final)
    R832720 (2009)
  • Abstract from PubMed
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Nikolaeva-Glomb L, Galabov AS, Kuz'min VE. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Structural Chemistry 2013;24(5):1665-1679.
    abstract available  
    R833825 (Final)
    R832720 (2009)
  • Abstract: Springer-Abstract
    Exit
  • Journal Article O'Shea SH, Schwarz J, Kosyk O, Ross PK, Ha MJ, Wright FA, Rusyn I. In vitro screening for population variability in chemical toxicity. Toxicological Sciences 2011;119(2):398-407.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Politi R, Rusyn I, Tropsha A. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicology and Applied Pharmacology 2014;280(1):177-189.
    abstract available   full text available
    R833825 (Final)
    R835166 (2014)
    R835166 (2016)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Abstract: ScienceDirect-Abstract
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Rusyn I, Daston GP. Computational toxicology: realizing the promise of the toxicity testing in the 21st century. Environmental Health Perspectives 2010;118(8):1047-1050.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: EHP-Full Text PDF
  • Abstract: EHP-Abstract & Full Text HTML
  • Journal Article Rusyn I, Gatti DM, Wiltshire T, Kleeberger SR, Threadgill DW. Toxicogenetics: population-based testing of drug and chemical safety in mouse models. Pharmacogenomics 2010;11(8):1127-1136.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Future Medicine-Abstract
    Exit
  • Journal Article Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicological Sciences 2012;127(1):1-9.
    abstract available   full text available
    R833825 (Final)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environmental Health Perspectives 2011;119(3):364-370.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: EHP-Full Text PDF
  • Abstract: EHP-Abstract & Full Text HTML
  • Journal Article Soldatow VY, LeCluyse EL, Griffith LG, Rusyn I. In vitro models for liver toxicity testing. Toxicology Research 2013;2(1):23-39.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Toxicology Research-Abstract
    Exit
  • Journal Article Staab JM, O’Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics 2010;11:123.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-Full Text PDF
    Exit
  • Journal Article Strychalski W, Adalsteinsson D, Elston TC. A cut-cell method for simulating spatial models of biochemical reaction networks in arbitrary geometries. Communications in Applied Mathematics and Computational Science 2010;5(1):31-53.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: MSP-Full Text PDF
    Exit
  • Abstract: MSP-Abstract
    Exit
  • Other: ResearchGate-Abstract & Full Text
    Exit
  • Journal Article Sun W, Buck MJ, Patel M, Davis IJ. Improved ChIP-chip analysis by a mixture model approach. BMC Bioinformatics 2009;10:173 (13 pp.).
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: BioMed Central-Full Text HTML
    Exit
  • Abstract: BioMed Central-Abstract
    Exit
  • Other: BioMed Central-Full Text PDF
    Exit
  • Journal Article Sun W, Xie W, Xu F, Grunstein M, Li K-C. Dissecting nucleosome free regions by a segmental semi-Markov model. PLoS One 2009;4(3):e4721 (10 pp.).
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS One-Full Text HTML
    Exit
  • Other: PLoS One-Full Text PDF
    Exit
  • Journal Article Sun W, Wright FA, Tang Z, Nordgard SH, Van Loo P, Yu T, Kristensen VN, Perou CM. Integrated study of copy number states and genotype calls using high-density SNP arrays. Nucleic Acids Research 2009;37(16):5365-5377.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Sun W, Wright FA. A geometric interpretation of the permutation p-value and its application in eQTL studies. Annals of Applied Statistics 2010;4(2):1014-1033.
    abstract available   full text available
    R833825 (Final)
  • Full-text: Project Euclid-Full Text PDF
    Exit
  • Abstract: Project Euclid-Abstract
    Exit
  • Other: ResearchGate-Abstract & Full Text
    Exit
  • Journal Article Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics 2012;68(1):1-11.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Wiley Online-Abstract
    Exit
  • Journal Article Sun W, Li L. Multiple loci mapping via model-free variable selection. Biometrics 2012;68(1):12-22.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Abstract: Wiley-Abstract
    Exit
  • Journal Article Sun W, Hu Y. eQTL mapping using RNA-seq data. Statistics in Biosciences 2013;5(1):198-219.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Springer-Abstract
    Exit
  • Journal Article Sushko I, Novotarskyi S, Korner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Muller KR, Xi L, Liu H, Yao X, Oberg T, Hormozdiari F, Dao P, Sahinalp C, Todeschini R, Polishchuk P, Artemenko A, Kuz’min V, Martin TM, Young DM, Fourches D, Muratov E, Tropsha A, Baskin I, Horvath D, Marcou G, Muller C, Varnek A, Prokopenko VV, Tetko IV. Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling 2010;50(12):2094-2111.
    abstract available   full text available
    R833825 (Final)
  • Abstract from PubMed
  • Full-text: ResearchGate-Abstract & Full Text PDF
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Wright FA, Shabalin AA, Rusyn I. Computational tools for discovery and interpretation of expression quantitative trait loci. Pharmacogenomics 2012;13(3):343-352.
    abstract available   full text available
    R833825 (Final)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text
    Exit
  • Abstract: Future Medicine-Abstract
    Exit
  • Journal Article Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W, Sullivan PF, Wright FA. seeQTL: a searchable database for human eQTLs. Bioinformatics 2012;28(3):451-452.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Xu K, Morgan KT, Todd Gehris A, Elston TC, Gomez SM. A whole-body model for glycogen regulation reveals a critical role for substrate cycling in maintaining blood glucose homeostasis. PLoS Computational Biology 2011;7(12):e1002272 (13 pp.).
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: PLoS-Full Text PDF
    Exit
  • Abstract: PLoS-Abstract & Full Text HTML
    Exit
  • Journal Article Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicology and Applied Pharmacology 2013;272(1):67-76.
    abstract available   full text available
    R833825 (Final)
    R835166 (2013)
    R835166 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ScienceDirect-Full Text HTML
    Exit
  • Other: ScienceDirect-Full Text PDF
    Exit
  • Journal Article Zhang X, Huang S, Sun W, Wang W. Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. Genetics 2012;190(4):1511-1520.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Genetics-Full Text HTML
    Exit
  • Other: Genetics-Full Text PDF
    Exit
  • Journal Article Zhou Y-H, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 2011;27(19):2672-2678.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Zhou Y-H, Barry WT, Wright FA. Empirical pathway analysis, without permutation. Biostatistics 2013;14(3):573-585.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Oxford Journals-Full Text HTML
    Exit
  • Abstract: Oxford Journals-Abstract
    Exit
  • Other: Oxford Journals-Full Text PDF
    Exit
  • Journal Article Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives 2008;116(4):506-513.
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
    R832720 (2007)
    R832720 (2008)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: EHP-Full Text HTML
  • Abstract: EHP-Abstract
  • Other: EHP-Full Text PDF
  • Journal Article Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives 2009;117(8):1257-1264.
    abstract available   full text available
    R833825 (2008)
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: EHP-Full Text PDF
  • Abstract: EHP-Abstract & Full Text HTML
  • Journal Article Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology 2009;22(12):1913-1921.
    abstract available   full text available
    R833825 (Final)
    R832720 (2009)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ACS-Full Text HTML
    Exit
  • Abstract: ACS-Abstract
    Exit
  • Other: ACS-Full Text PDF
    Exit
  • Journal Article Zou F, Lee S, Knowles MR, Wright FA. Quantification of population structure using correlated SNPs by shrinkage principal components. Human Heredity 2010;70(1):9-22.
    abstract available   full text available
    R833825 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: Karger-Full Text PDF
    Exit
  • Abstract: Karger-Abstract
    Exit
  • Supplemental Keywords:

    Modeling, mechanistic models, hybrid models, bioinformatics, metabolism, in vitro, eQTL, genetics, genomics, screening, toxicity, QSAR, machine learning, short term assays, toxicogenomics

    Relevant Websites:

    Carolina Center for Computational Toxicology Exit
    xCEED Exit
    Chembench Exit

    Progress and Final Reports:

    Original Abstract
  • 2008 Progress Report
  • 2009
  • 2010